Connecting to a fixed network using an extensible drone-based, dynamic network

ABSTRACT

A method for dynamic and extensible creation of an extensible wireless network, using a set of drones that individually support server processes. The drones interact with one another, exchanging information, type of coverage, type and amount of throughput, location, etc. A control node connects to a wired network. The node operates a leader election protocol, captures state information from the drones, and positions/re-positions the drones as necessary. Drones are flown in to position and then engaged as necessary to stretch/adapt the coverage as necessary. The drone&#39;s power utilization is monitored and its coverage area modified as necessary to optimize power utilization. The control node performs drone-based coverage/power utilization computations, and attempts to apply the appropriate location assignments to provide maximum network coverage (extensibility) while also preserving drone-specific power (battery) utilization. The approach herein can be used to supplement existing networks during events, migrations of populations during work hours, etc.

BACKGROUND Technical Field

This application relates generally to data delivery over networks.

Brief Description of the Related Art

Distributed computer systems are well-known in the prior art. One suchdistributed computer system is a “content delivery network” (CDN) or“overlay network” that is operated and managed by a service provider.The service provider typically provides the content delivery service onbehalf of third parties (customers) who use the service provider'sshared infrastructure. A distributed system of this type typicallyrefers to a collection of autonomous computers linked by a network ornetworks, together with the software, systems, protocols and techniquesdesigned to facilitate various services, such as content delivery, webapplication acceleration, or other support of outsourced origin siteinfrastructure. A CDN service provider typically provides servicedelivery through digital properties (such as a website), which areprovisioned in a customer portal and then deployed to the network.

It is known to extend a CDN to interoperate with a peer-to-peer (P2P)network. One such hybrid solution is described in U.S. Pat. No.8,332,484. In this approach, the content delivery network includes amapping system for directing requests to CDN servers. One or more peermachines become associated with the CDN, and the CDN mapping system isthen used to enable a given peer to locate another peer in the P2Pnetwork, and/or a CDN server. Using this hybrid approach, CDN customercontent may be delivered from the CDN edge network, from the P2Pnetwork, or from both networks. In one embodiment, customer content isuploaded to the CDN and stored in the edge network, or in a storagenetwork associated therewith. The CDN edge network is then used to primethe P2P network, which may be used to take over some of the contentdelivery requirements for the customer content. The decision of whetherto use edge network or peer network resources for delivery may be basedon load and traffic conditions.

A mesh network a local network topology in which the infrastructurenodes (i.e. bridges, switches, and other infrastructure devices) connectdirectly, dynamically and non-hierarchically to as many other nodes aspossible and cooperate with one another to efficiently route datafrom/to clients. This lack of dependency on one node allows for everynode to participate in the relay of information. Mesh networksdynamically self-organize and self-configure, which can reduceinstallation overhead. The ability to self-configure enables dynamicdistribution of workloads, particularly in the event a few nodes shouldfail. This in turn contributes to fault-tolerance and reducedmaintenance costs.

Demand for network connectivity is not always generated from static(fixed) sources. In certain installations, e.g., concerts, stadiums,military, first responder scenarios, the “scope” of the network isill-defined or not capable of being defined in advance. Thus, it is notpossible for a service provider—including, without limitation, anoverlay network provider—to position network infrastructure (to serviceuser requirements) in advance. That said, it is known in the prior artto provide wireless ad hoc networks using drones, but these approacheshave not been extended, e.g., to create an extensible network for thewired Internet itself.

BRIEF SUMMARY

The subject matter herein provides for a method and system for dynamicand extensible creation of an extensible wireless network, preferablyvia a set of drones, which individually support/host server processes.The drones interact with a control node/host (or one another),exchanging information such as type of coverage, type and amount ofthroughput, location, etc. A control node, typically attached at a 5Ghotspot, connects to the wired (terrestrial) network, e.g., thepublicly-routable Internet. The control node operates a leader electionprotocol, captures state information from the drones, andpositions/re-positions the drones as necessary (or on-the-fly) to createan extensible wireless network. Drones are flown in to position, e.g.,landed on rooftops, and then engaged as necessary to stretch/adapt thecoverage as necessary. The drone's power utilization is monitored andits coverage area modified as necessary to optimize power utilization.The control node thus performs drone-based coverage/power utilizationcomputations, and attempts to apply the appropriate location assignmentsto provide maximum network coverage (extensibility) while alsopreserving drone-specific power (battery) utilization and maintaining aquality of service (QoS).

The drones are used to create a network on-demand along with anappropriate set of content. This approach can be used to supplementexisting networks during events, migrations of populations during workhours, etc. In lieu of drones, satellites may be used in whole or inpart to provide the networking infrastructure for the extensiblenetwork.

The foregoing has outlined some of the more pertinent features of thedisclosed subject matter. These features should be construed to bemerely illustrative. Many other beneficial results can be attained byapplying the disclosed subject matter in a different manner or bymodifying the subject matter as will be described.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the subject matter herein and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawings, in which:

FIG. 1 depicts a cellular mobile network model;

FIG. 2 depicts a network of drones connected in a mesh network toprovide 5G and/or Wi-Fi coverage in a coverage area; and

FIG. 3 depicts using an extensible drone-based, dynamic network coupledto a public wired (terrestrial network) according to this disclosure.

DETAILED DESCRIPTION

By way of background, FIG. 1 shows a simplified architectural model of acellular mobile network 100 (e.g., a 4G network). In the drawing, aradio resource controller (RRC) 102 resides in a base station (radiotower) 104. RRC 102 is the entity that manages allocation of the radiolink between an end user and the base station. The RRC manages the radiolink such that the link between the radio tower and end user isallocated only when needed. Also depicted is the packet core network106, which serves the traffic between the RRC and a packet gateway 108.The communication protocols in the packet core network are proprietaryin the sense that they are operated in a black box-style, i.e., notexposed to the other network protocol-speaking equipment outside thepacket core network. The packet gateway 108 is the interface for therest of the Internet 110. It is the intermediary between the packet corenetwork and the Internet. 5G is the fifth generation wireless technologyfor digital cellular networks that began wide deployment in 2019. “5G”can also be referred to as New Radio (NR) access. As with previousstandards, the covered areas are divided into regions called “cells,”serviced by individual antennas. The frequency spectrum of 5G is dividedinto millimeter waves, mid-band and low-band. Low-band uses a similarfrequency range as the predecessor, 4G.

As used herein, a drone” refers to an unmanned vehicle that is pilotedby remote control or onboard computers. Drones typically are aerial, butthey may also include unmanned land-based vehicles, unmanned water-based(floating) vehicles, or even unmanned spacecraft. Drones may be operatedunder remote control by a human operator, or fully or intermittentlyautonomously by onboard one or more onboard computers. More generally, a“drone” is any movable component that supports a computing capabilityand network communications capability.

FIG. 2 depicts a network 200 of drones 202 connected in a mesh network205 to provide 5G and/or Wi-Fi coverage in an area 204. The coveragearea 2-4 may have any desired shape, and is typically shaped based onthe actual deployment of the drones as well as environmental conditions.The coverage area may be contiguous, or it may be a collection ofdisconnected coverage areas and may have holes or gaps in the coveragearea within the perimeter of the coverage area. The network 200 is afully-interconnected mesh network, although at any particular timeand/or location within the network there may be any level ofinterconnectivity among the drones. There may be multiple independentdrone networks (each comprising one or more drones), with one dronenetwork overlaying or otherwise coupled to another drone network.Typically, drones are battery powered, and thus power management is animportant consideration.

As depicted in FIG. 3, and according to this disclosure, a control node306 has connectivity to an existing network, such as terrestrial network308. Preferably, the control node operates as a 5G node hotspot. One ormore drone nodes 302 have connectivity to the control node 306, althoughthe number and location of the drone nodes 304 may vary over time andspace 304. Collectively, the drone nodes 302 maintain connectivity tothe control node 306 and thus form an ad hoc mesh network 305. Thenetwork 305 is an extensible drone-based, dynamic network (in the sensethat the constituent drones may come and go, over time, or as drone(s)lost connectivity), and this drone network is coupled via the controlnode 306 to the network 308, which is typically a wired (terrestrial)network. Network 308 may be packet-based, such as the public-routableInternet. In the alternative, the network 308 may be a radio accessnetwork (RAN). A drone individually supports/hosts one or more serverprocesses 310, e.g., an edge server associated with a content deliverynetwork. More generally, the drone server process provides one or moreservices with respect to one or more requesting clients that are coupledto a server process. The drones interact with one another, exchanginginformation, type of coverage, type and amount of throughput, quality ofservice (QoS), location, etc. The control node 306, typically attachedat a 5G hotspot, connects to the wired (terrestrial) network 308. Thecontrol node operates a leader election protocol, captures stateinformation from the drones that are then present in the ad hoc network(and capable of maintaining connectivity to the control node), andselectively positions/re-positions the drones 302 as necessary (oron-the-fly) to create and/or maintain the extensible wireless network.Drones are flown in to position and then engaged as necessary tostretch/adapt the coverage as necessary. The drone's power utilizationis monitored and its coverage area modified as necessary to optimizepower utilization. The control node thus performs drone-basedcoverage/power utilization computations, and attempts to apply theappropriate location assignments to provide maximum network coverage(extensibility) while also preserving drone-specific power (battery)utilization and maintaining a quality of service (QoS).

The particular placement and organization of the drones by the controlnode is carried out according to one or more positioning algorithms. Inone duplex-based arrangement, clients (UEs), such as mobile device 312,are making requests for service that are being received and managed bythe drone server processes, and meanwhile the control node is managingthe drones themselves. Content requests received at a drone serverprocess may be forwarded through the control node to the couplednetwork, and responses received from the coupled network then returnedto the requesting clients via the drone network. Thus, typically, thedrone network operates in a full duplex mode. (RAN-based networks arenot full duplex).

The control node is continuously receiving state and status informationfrom one or more drones that maintain connectivity to the control node.Although the control node is shown as connected between the ad hoc dronenetwork and the wired network, theoretically the control node may be anynode that has the capability of maintaining connectivity to one or moreof the drones of a drone network or subnetwork. In the usual case, thecontrol node is a 5G hotspot that is coupled directly to the wirednetwork. As noted, the control node receives sensor data from each ofthe drones. Thus, at any point-in-time, the control node knows thephysical coverage area that is available to be surfaced, as well as thequality-of-service for that area. The control node operates tocontinuous position and/or re-position the then-connected drones,preferably to optimize QoS for the connected devices, while preservingbattery life of the drones. Generalizing, the control node operates toprovide optimal network coverage (extensibility) while also preservingdrone-specific power (battery) utilization and maintaining quality ofservice (QoS) for one or more connected clients.

Representative algorithms that may be used for this purpose include,without limitation, maximum flow algorithms such as Ford-Fulkerson(FFK). FFK is a greedy algorithm that computes the maximum flow in aflow network. In operation, the control node continuously models theclients and drones as a flow network and positions/re-positions thedrones as necessary to provide the maximum flow that maximizes batterylife, provides the appropriate extensibility of the network, at thedesired quality-of-service.

As used herein, a client may be any user equipment (UE). Generalizing, amobile device (or UE) can refer to any type of wireless device that cancommunicate with a radio network node in a cellular or mobilecommunication system. Examples of mobile devices can include, but arenot limited to, a target device, a device to device (D2D) UE, a machinetype UE or a UE capable of machine to machine (M2M) communication, aPersonal Digital Assistant (PDA), a tablet or pad (e.g., an electronictablet or pad), a mobile terminal, a cellular and/or smart phone, acomputer (e.g., a laptop embedded equipment (LEE), a laptop mountedequipment (LME), or other type of computer), a dongle (e.g., a UniversalSerial Bus (USB) dongle), an electronic gaming device, a deviceassociated or integrated with a vehicle (e.g., automobile, train,motorcycle, bicycle, ship, plane, . . . ), a motorized device (e.g.,drone), or other entity, and so on.

The terms “network node device,” “network node,” and “network device”can be interchangeable with (or include) a network, a network controlleror any number of other network components. Further, as utilized herein,the non-limiting term radio network node, or network node (e.g., networkdevice, network node device) can be used herein to refer to any type ofnetwork node serving communications devices and/or connected to othernetwork nodes, network elements, or another network node from which thecommunications devices can receive a radio signal. In cellular radioaccess networks (e.g., universal mobile telecommunications system (UMTS)networks), network devices can be referred to as base transceiverstations (BTS), radio base station, radio network nodes, base stations,NodeB, eNodeB (e.g., evolved NodeB), and so on. In 5G terminology, thenetwork nodes can be referred to as gNodeB (e.g., gNB) devices. Networkdevices also can comprise multiple antennas for performing varioustransmission operations (e.g., MIMO operations). A network node cancomprise a cabinet and other protected enclosures, an antenna mast, andactual antennas. Network devices can serve several cells, and associatedsectors (e.g., a sector can comprise one or more cells), depending onthe configuration and type of antenna. Network node devices can be, forexample, Node B devices, base station (BS) devices, access point (AP)devices, TRPs, and radio access network (RAN) devices. Other examples ofnetwork node devices can include multi-standard radio (MSR) nodedevices, comprising: an MSR BS, a gNodeB, an eNodeB, a networkcontroller, a radio network controller (RNC), a base station controller(BSC), a relay, a donor node controlling relay, a BTS, an AP, atransmission point, a transmission node, a Remote Radio Unit (RRU), aRemote Radio Head (RRH), nodes in distributed antenna system (DAS), andthe like.

More generally, the techniques described herein are provided using a setof one or more computing-related entities (systems, machines, processes,programs, libraries, functions, or the like) that together facilitate orprovide the described functionality described above. In a typicalimplementation, a representative machine on which the software executescomprises commodity hardware, an operating system, an applicationruntime environment, and a set of applications or processes andassociated data, that provide the functionality of a given system orsubsystem. As described, the functionality may be implemented in astandalone machine, or across a distributed set of machines. Thefunctionality may be provided as a service, e.g., as a SaaS solution. Anedge compute instance may be supported in a virtual environment.

While the above describes a particular order of operations performed bycertain embodiments of the invention, it should be understood that suchorder is exemplary, as alternative embodiments may perform theoperations in a different order, combine certain operations, overlapcertain operations, or the like. References in the specification to agiven embodiment indicate that the embodiment described may include aparticular feature, structure, or characteristic, but every embodimentmay not necessarily include the particular feature, structure, orcharacteristic.

While the disclosed subject matter has been described in the context ofa method or process, the subject disclosure also relates to apparatusfor performing the operations herein. This apparatus may be speciallyconstructed for the required purposes, or it may comprise ageneral-purpose computer selectively activated or reconfigured by acomputer program stored in the computer. Such a computer program may bestored in a computer readable storage medium, such as, but is notlimited to, any type of disk including an optical disk, a CD-ROM, and amagnetic-optical disk, a read-only memory (ROM), a random access memory(RAM), a magnetic or optical card, or any type of media suitable forstoring electronic instructions, and each coupled to a computer systembus. While given components of the system have been describedseparately, one of ordinary skill will appreciate that some of thefunctions may be combined or shared in given instructions, programsequences, code portions, and the like.

Preferably, the functionality is implemented in an application layersolution, although this is not a limitation, as portions of theidentified functions may be built into an operating system (running TCP)or the like.

The functionality may be implemented with other application layerprotocols besides HTTPS, such as SSL VPN, or any other protocol havingsimilar operating characteristics.

The techniques herein may be used irrespective of the traffic type.There is no limitation on the type of computing entity that mayimplement the client-side or server-side of the connection. Anycomputing entity (system, machine, device, program, process, utility, orthe like) may act as the client or the server.

Finally, while given components of the system have been describedseparately, one of ordinary skill will appreciate that some of thefunctions may be combined or shared in given instructions, programsequences, code portions, and the like.

The techniques herein provide for improvements to a technology ortechnical field, namely, overlay networking, as well as improvements tothe functioning of edge server itself, namely, by extending itsconventional functionality as has been described.

Local data collection techniques (for supporting local model building)include, without limitation, active and passive data collection, datatraffic monitoring, packet inspection, application layer-based,operating system kernel-based, and otherwise.

The various aspects described herein can relate to 5G (New Radio), whichcan be deployed as a standalone radio access technology or as anon-standalone radio access technology assisted by another radio accesstechnology, such as Long Term Evolution (LTE). It should be noted thatalthough various aspects and embodiments have been described herein inthe context of 5G, Universal Mobile Telecommunications System (UMTS),and/or Long Term Evolution (LTE), or other next generation networks, thedisclosed aspects are not limited to 5G, a UMTS implementation, and/oran LTE implementation as the techniques can also be applied in 3G, 4G,or LTE systems. For example, aspects or features of the disclosedembodiments can be exploited in substantially any wireless communicationtechnology. Such wireless communication technologies can include UMTS,Code Division Multiple Access (CDMA), Wi-Fi, Worldwide Interoperabilityfor Microwave Access (WiMAX), General Packet Radio Service (GPRS),Enhanced GPRS, Third Generation Partnership Project (3GPP), LTE, ThirdGeneration Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB),High Speed Packet Access (HSPA), Evolved High Speed Packet Access(HSPA+), High-Speed Downlink Packet Access (HSDPA), High-Speed UplinkPacket Access (HSUPA), Zigbee, or another IEEE 802.XX technology. Suchwireless communication technologies also can include Bluetooth or nearfield communication (NFC) technologies. Additionally, substantially allaspects disclosed herein can be exploited in legacy telecommunicationtechnologies. Further, the various aspects can be utilized with anyRadio Access Technology (RAT) or multi-RAT system where the mobiledevice operates using multiple carriers (e.g., LTE Frequency DivisionDuplexing (FDD)/Time-Division Duplexing (TDD), Wideband Code DivisionMultiplexing Access (WCMDA)/HSPA, Global System for MobileCommunications (GSM)/GSM EDGE Radio Access Network (GERAN), Wi Fi,Wireless Local Area Network (WLAN), WiMax, CDMA2000, and so on).

As used herein, a drone includes computing and communications hardwareand software.

As used herein, the control node is a computing platform or entity thatincludes computing and communications hardware and software.

As used herein, a UE (e.g., a mobile device) is a computing entity thatincludes computing and communications hardware and software.

One or more drone networks may connect with one another and then to thecontrol node.

A drone may support/host an application instance.

Preferably, one or more drones are parked when possible to preservebattery power.

As needed, a drone-based mesh network as described herein isreconfigured as necessary by rotating one or more drones in and out ofservice, e.g., as particular drones need to be recharged or otherwisefail, or for other operational, management, administrative or securityreasons.

The drone-based mesh network as described herein also may be createddynamically, e.g., to bridge connectivity gaps, to help repair failures,etc., with respect to some other network.

Different drones within the drone-based mesh network may have differentavailable power and thus may be assigned different tasks, e.g., based ontheir available processing or other capabilities. Thus, for example, adrone at a given network location (with significant power available) maybe tasked to perform more computational or storage-intensive tasks(e.g., video processing/compression) as compared to one or more otherdrones located elsewhere. This type of configuration enables bandwidthavailable to the other drones to be used for other purposes. Moregenerally, different compute functions are positioned at differentplaces in the mesh based on power available, processing capability, orsimilar considerations. As a further variant, indirect routing may beused selectively across the mesh to enhance performance.

There is no requirement that the control node perform all of theplanning for the entire mesh network; in an alternative embodiment, thecontrol node is responsible for just a portion thereof. In a variantembodiment, a particular drone is configured to perform theabove-described control operations and executes the positioningalgorithm for its portion of the network. More generally, multipledrones (or control devices associated therewith) plan and enforce theirrespective portions of the network independently from one another.

Having described the subject matter herein, what is claimed is set forthas follows.

1. A method of networking, comprising: receiving information from each of one or more drones, the information comprising a type of coverage, a type and amount of throughput, a location, and battery data, wherein a drone provides a service; responsive to the information and one or more service requests, executing a positioning algorithm that positions the one or more drones into an extensible mesh network by attempting to balance an optimal coverage area associated with the one or more drones, an optimal power utilization for each of the drones, and a desired quality-of-service associated with the one or more service requests.
 2. The method as described in claim 1 further including coupling the extensible mesh network to a wired network.
 3. The method as described in claim 2 wherein the wired network is the publicly-routable Internet.
 4. The method as described in claim 2 wherein the positioning algorithm is executed in a control node that couples the extensible mesh network to the wired network.
 5. The method as described in claim 4 wherein the positioning algorithm is a maximum flow algorithm.
 6. The method as described in claim 5 wherein the maximum flow algorithm is a Ford-Fulkerson algorithm.
 7. The method as described in claim 1 wherein the service is content delivery or application acceleration.
 8. The method as described in claim 1 wherein the extensible mesh network is created on-the-fly in response to a given occurrence.
 9. The method as described in claim 1 wherein the extensible mesh network provides network coverage to an area that is not serviced by a terrestrial wired network.
 10. The method as described in claim 1 wherein service requests originate from user equipment (UE).
 11. The method as described in claim 1 further including reconfiguring the extensible mesh network dynamically upon a given occurrence.
 12. The method as described in claim 11 wherein the extensible mesh network is reconfiguring by rotating one or more drones in and out of extensible mesh network.
 13. The method as described in claim 1 wherein first and second drones in the extensible mesh network execute distinct compute functions as a function of available power.
 14. Apparatus, comprising: a processor; computer memory holding computer program instructions executed by the processor, the computer program instructions including program code configured to: receive information from each of one or more drones, the information comprising a type of coverage, a type and amount of throughput, a location, and battery data, wherein a drone provides a service; responsive to the information and one or more service requests, execute a positioning algorithm that positions the one or more drones into an extensible mesh network by attempting to balance an optimal coverage area associated with the one or more drones, an optimal power utilization for each of the drones, and a desired quality-of-service associated with the one or more service requests.
 15. The apparatus as described in claim 14 wherein the program code is further configured to couple the extensible mesh network to a wired network.
 16. The apparatus as described in claim 14 wherein the positioning algorithm is a maximum flow algorithm.
 17. The apparatus as described in claim 16 wherein the maximum flow algorithm is a Ford-Fulkerson algorithm.
 18. The apparatus as described in claim 14 wherein the service is content delivery or application acceleration.
 19. The apparatus as described in claim 14 wherein the extensible mesh network is created on-the-fly in response to a given occurrence.
 20. The apparatus as described in claim 11 wherein first and second drones in the extensible mesh network execute distinct compute functions as a function of available power. 